Security Incidents Analysis: Global Patterns and Trends
1 Introduction
This analysis explores patterns and trends in global security incidents, identifying hotspots and tracking how they’ve evolved over time. By examining data on security incidents from around the world, we can better understand which regions face the greatest challenges and how these challenges have shifted in recent years.
Security incidents can range from physical attacks to cyber threats, and their distribution isn’t uniform across the globe. Understanding these patterns can help security professionals, policymakers, and researchers allocate resources effectively and develop targeted strategies to mitigate risks.
The following analysis uses data visualization techniques to uncover insights about: - The geographic distribution of security incidents - How incident patterns have changed over time - Which countries represent security hotspots, both historically and recently
1.1 Dataset Overview
Our analysis begins with a cleaned dataset of security incidents collected from various sources. Let’s examine the scope of our data:
Code
print(f"Dataset contains {len(df)} incidents across {df['country'].nunique()} countries")print(f"Time period covered: {df['year'].min()} to {df['year'].max()}")
Dataset contains 4314 incidents across 95 countries
Time period covered: 1997 to 2025
This extensive dataset allows us to perform comprehensive analysis across both geographic and temporal dimensions. The data has been preprocessed to ensure consistency in country names, coordinate information, and incident classifications.
2 Global Distribution of Security Incidents
Security incidents aren’t distributed evenly across the world. Some regions experience higher concentrations due to various factors including geopolitical tensions, economic disparities, and historical conflicts. Visualizing this distribution helps us identify global patterns.
2.1 Interactive Global Incident Map
The map below displays incidents across the globe, with colors indicating the severity based on the number of people affected: - Blue: No reported casualties - Green: 1-5 affected individuals - Orange: 6-20 affected individuals - Red: More than 20 affected individuals
Clustering is used to manage dense areas where multiple incidents occurred in close proximity.
Code
def create_incidents_map(data):""" Create an interactive folium map with clustered markers for security incidents. Args: data: DataFrame containing incident data with latitude and longitude coordinates Returns: folium.Map: Interactive map with clustered markers """# Calculate center coordinates for the map center_lat = data['latitude'].mean() center_lon = data['longitude'].mean()# Create base map incidents_map = folium.Map(location=[center_lat, center_lon], zoom_start=2)# Add marker cluster marker_cluster = MarkerCluster().add_to(incidents_map)# Filter for valid coordinates valid_coords = data[data['latitude'].notna() & data['longitude'].notna()]# Define color function based on number of affected peopledef get_color(affected):if pd.isna(affected) or affected ==0:return'blue'elif affected <=5:return'green'elif affected <=20:return'orange'else:return'red'# Add markers for each incidentfor _, row in valid_coords.iterrows(): popup_text =f""" <b>Country:</b> {row['country']}<br> <b>Year:</b> {row['year']}<br> <b>Total Affected:</b> {row['total_affected']}<br> <b>Attack Type:</b> {row['means_of_attack'] if'means_of_attack'in row and pd.notna(row['means_of_attack']) else'Unknown'}<br> """ folium.CircleMarker( location=[row['latitude'], row['longitude']], radius=5, popup=folium.Popup(popup_text, max_width=300), fill=True, fill_opacity=0.7, color=get_color(row['total_affected']), fill_color=get_color(row['total_affected']) ).add_to(marker_cluster)return incidents_map
Code
# Create and display the global incidents mapglobal_incidents_map = create_incidents_map(df)map_filename ="images/global_security_incidents_map.html"global_incidents_map.save(map_filename)global_incidents_map
Make this Notebook Trusted to load map: File -> Trust Notebook
The interactive map reveals several important patterns: - Incidents tend to cluster in certain regions, particularly in conflict zones and areas with political instability - Urban centers often show higher concentrations of incidents - The distribution of high-severity incidents (red markers) isn’t uniform, suggesting regional differences in the nature of security threats
You can zoom in on specific regions and click on individual markers to get more details about each incident.
3 Temporal Trends in Security Incidents
Security landscapes evolve over time. By examining how incident patterns change year by year, we can identify emerging hotspots and areas where security might be improving.
3.1 Incidents by Country Over Time
The animated choropleth map below shows how the distribution of security incidents has shifted over the years. Darker colors indicate higher numbers of incidents.
This visualization reveals several key insights: - The global distribution of security incidents has shifted significantly over time - Some countries that were previously security hotspots have shown improvement - New areas of concern have emerged in recent years - The total number of recorded incidents shows notable year-to-year variation
You can use the play button to animate the map through time, or manually select specific years using the slider.
4 Annual Incident Trends
To better understand the overall temporal pattern of security incidents, we can examine the yearly totals across all countries.
Code
# Aggregate incidents by yearyear_incidents = df.groupby('year').size().reset_index(name='incidents')year_incidents['year'] = year_incidents['year'].astype(str)# Create interactive bar chartfig2 = px.bar( year_incidents, x='year', y='incidents', title='Interactive Security Incidents by Year', labels={'incidents': 'Number of Incidents', 'year': 'Year'}, height=400)# Configure layoutfig2.update_layout( title={'text': 'Interactive Security Incidents by Year','y': 0.95,'x': 0.5,'xanchor': 'center','yanchor': 'top' }, xaxis=dict(rangeslider=dict(visible=True), type='category'), bargap=0.1, template='plotly_white')# Save and display figurefig2.write_html("images/interactive_yearly_incidents_barchart.html")fig2.show()
The interactive bar chart shows: - Years with notable spikes in security incidents - Overall trends in global security over time - Periods of relative stability and instability
The rangeslider at the bottom allows you to focus on specific time periods for more detailed analysis. Several factors might explain the patterns observed, including: - Major global events and conflicts - Changes in reporting practices - Implementation of new security measures - Shifts in geopolitical landscapes
5 Identifying Security Hotspots
Not all countries experience security incidents at the same rate. By identifying which nations have faced the highest numbers of incidents, we can focus attention on areas that may require additional security resources and intervention.
5.1 Countries with Most Incidents: All-Time Analysis
First, let’s look at which countries have experienced the most security incidents over the entire period covered by our dataset:
Code
# Get total incidents by country for all timetotal_by_country = df.groupby('country').size().reset_index(name='total_incidents')total_by_country = total_by_country.sort_values('total_incidents', ascending=False)top15_countries = total_by_country.head(15)# Create bar chart for all-time top countriesfig_top_all_time = px.bar( top15_countries, x='country', y='total_incidents', title='Top 15 Countries by Security Incidents (All Time)', labels={'total_incidents': 'Number of Incidents', 'country': 'Country'}, color='total_incidents', color_continuous_scale='Viridis', height=450)# Configure layoutfig_top_all_time.update_layout( title={'text': 'Top 15 Countries by Security Incidents (All Time)','y': 0.95,'x': 0.5,'xanchor': 'center','yanchor': 'top','font': {'size': 20} }, xaxis={'categoryorder': 'total descending', 'tickangle': 45}, coloraxis_showscale=False)# Display the figurefig_top_all_time.show()# Save figurefig_top_all_time.write_html("images/top_countries_all_time.html")
This visualization highlights the countries that have historically been most affected by security incidents. Several factors might contribute to a country appearing on this list: - Long-standing regional conflicts - Political instability - Higher population (which can increase the absolute number of incidents) - More comprehensive reporting of incidents
5.2 Countries with Most Incidents: Recent Trends
Historical patterns don’t always reflect current realities. To identify emerging security hotspots, we need to focus on more recent data. The following analysis examines incident patterns over the past 10 years:
Code
# Get incidents for the last 10 yearscurrent_year = df['year'].max()ten_years_ago = current_year -10recent_df = df[df['year'] >= ten_years_ago]# Calculate top countries for recent periodrecent_by_country = recent_df.groupby('country').size().reset_index(name='recent_incidents')recent_by_country = recent_by_country.sort_values('recent_incidents', ascending=False)top15_recent = recent_by_country.head(15)# Create bar chart for recent top countriesfig_top_recent = px.bar( top15_recent, x='country', y='recent_incidents', title=f'Top 15 Countries by Security Incidents (Last 10 Years: {ten_years_ago}-{current_year})', labels={'recent_incidents': 'Number of Incidents', 'country': 'Country'}, color='recent_incidents', color_continuous_scale='Viridis', height=600)# Configure layoutfig_top_recent.update_layout( title={'text': f'Top 15 Countries by Security Incidents (Last 10 Years: {ten_years_ago}-{current_year})','y': 0.95,'x': 0.5,'xanchor': 'center','yanchor': 'top','font': {'size': 20} }, xaxis={'categoryorder': 'total descending', 'tickangle': 45}, coloraxis_showscale=False)# Display the figurefig_top_recent.show()# Save figurefig_top_recent.write_html("images/top_countries_recent.html")
This recent trends analysis shows: - Countries that have experienced deteriorating security situations in the past decade - Nations that continue to face persistent security challenges - Emerging hotspots that may not have appeared in historical data
By comparing this visualization with the all-time analysis, we can identify significant shifts in global security patterns.
6 Comparing Historical and Recent Hotspots
To better understand how security landscapes have changed, let’s directly compare the countries that appear in our all-time and recent top 15 lists:
Code
# Find overlapping countries in both top 15 listsall_time_set =set(top15_countries['country'])recent_set =set(top15_recent['country'])matches = all_time_set.intersection(recent_set)# Print resultsprint(f"Number of countries appearing in both top 15 lists: {len(matches)}")print("Countries appearing in both lists:")for country insorted(matches):print(f"- {country}")# Countries only in all-time listall_time_only = all_time_set - recent_setif all_time_only:print("\nCountries in all-time top 15 but not in recent top 15:")for country insorted(all_time_only):print(f"- {country}")# Countries only in recent listrecent_only = recent_set - all_time_setif recent_only:print("\nCountries in recent top 15 but not in all-time top 15:")for country insorted(recent_only):print(f"- {country}")
Number of countries appearing in both top 15 lists: 13
Countries appearing in both lists:
- Afghanistan
- Central African Republic
- DR Congo
- Ethiopia
- Mali
- Myanmar
- Nigeria
- Occupied Palestinian Territories
- Somalia
- South Sudan
- Sudan
- Syrian Arab Republic
- Yemen
Countries in all-time top 15 but not in recent top 15:
- Iraq
- Pakistan
Countries in recent top 15 but not in all-time top 15:
- Cameroon
- Haiti
This comparison reveals three important categories:
Persistent Hotspots: Countries appearing in both lists have faced ongoing security challenges throughout the period covered by our dataset. These nations may require sustained international attention and support.
Improving Situations: Countries that appear in the all-time list but not in the recent list have likely experienced security improvements. Understanding the factors behind these positive trends could provide valuable insights for improving security elsewhere.
Emerging Concerns: Countries that appear in the recent list but not in the all-time list represent emerging security challenges. Early intervention in these areas might prevent further deterioration.
7 Conclusion
This analysis has revealed significant patterns in the global distribution of security incidents, both geographically and temporally. Key takeaways include:
Security incidents aren’t distributed uniformly across the globe, with clear hotspots in specific regions
The global security landscape has evolved considerably over time, with some countries experiencing improvements while others face deteriorating conditions
Both historical patterns and recent trends are important when assessing security situations
The interactive visualizations presented here allow for exploration of specific regions and time periods of interest
These insights can help inform security policies, resource allocation, and intervention strategies. By understanding where and when security incidents are most likely to occur, stakeholders can work more effectively to mitigate risks and improve global security.
Future work could expand on this analysis by: - Incorporating additional data sources for more comprehensive coverage - Analyzing specific types of security incidents separately - Examining correlations with socioeconomic and political factors - Developing predictive models to forecast emerging security hotspots
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